Although the aforementioned two-step approaches have achieved satisfactory pronunciation scoring or mispronunciation detection results, the performances heavily rely on the size of labeled scoring samples, that is not always practical in many real-world applications. Meanwhile, the non-native data collection and labeling process is costly and has scalability issues [1]}. Take the recently released public free dataset Speechocean762 [2]} for example, only 5,000 sentences, 31,816 words and 95,078 phones have been assigned with human scores. Another public dataset L2-ARCTIC [3]} has collected 11,026 utterances including 97,000 words and 349,000 phone segments. The comparison in [4]} show that the largest non-native corpus contains 90,841 utterances, but it is not publicly available. To tackle the limited non-native data problem, data augmentation techniques have been investigated in some previous work [1]}, [6]}, [7]}. In [6]}, Text-To-Speech (TTS) was used to synthesize “incorrect stress" samples on top of modified lexical stress. In [1]}, the author created negative training samples by modifying the canonical text as the modified text forms a mismatched pair with its original word-level speech. More recently, authors in [7]} collected a large number of unlabeled non-native data for pretraining sentence-level scorer with GOP scores. It avoids time-consuming labeling, but non-native data collection is still a challenge task.
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